Meta-Heuristics with Local Search for Retail Shelf Allocation Optimization

• Main Authors: LIM, Andrew, Rodrigues, Brian, ZHANG, Xingwen
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2004
• 主題: retail; shelf allocation; metaheuristics; Operations and Supply Chain Management
• 在線閱讀: https://ink.library.smu.edu.sg/lkcsb_research/2279; https://doi.org/10.1287/mnsc.1030.0165
• 機構: Singapore Management University

Efficient shelf-space allocation can provide retailers with a competitive edge. While there has been little study on this subject, there is great interest in improving product allocation in the retail industry. This paper examines a practicable linear allocation model for optimizing shelf-space allocation. It extends the model to address other requirements such as product groupings and nonlinear profit functions. Besides providing a network flow solution, we put forward a strategy that combines a strong local search with a metaheuristic approach to space allocation. This strategy is flexible and efficient, as it can address both linear and nonlinear problems of realistic size while achieving near-optimal solutions through easily implemented algorithms in reasonable timescales. It offers retailers opportunities for more efficient and profitable shelf management, as well as higher-quality planograms. [PUBLICATION ABSTRACT]